Abstract
Abstract. Equilibrium climate sensitivity (ECS) has been directly estimated using reconstructions of past climates that are different than today's. A challenge to this approach is that temperature proxies integrate over the timescales of the fast feedback processes (e.g., changes in water vapor, snow, and clouds) that are captured in ECS as well as the slower feedback processes (e.g., changes in ice sheets and ocean circulation) that are not. A way around this issue is to treat the slow feedbacks as climate forcings and independently account for their impact on global temperature. Here we conduct a suite of Last Glacial Maximum (LGM) simulations using the Community Earth System Model version 1.2 (CESM1.2) to quantify the forcing and efficacy of land ice sheets (LISs) and greenhouse gases (GHGs) in order to estimate ECS. Our forcing and efficacy quantification adopts the effective radiative forcing (ERF) and adjustment framework and provides a complete accounting for the radiative, topographic, and dynamical impacts of LIS on surface temperatures. ERF and efficacy of LGM LIS are −3.2 W m−2 and 1.1, respectively. The larger-than-unity efficacy is caused by the temperature changes over land and the Northern Hemisphere subtropical oceans which are relatively larger than those in response to a doubling of atmospheric CO2. The subtropical sea-surface temperature (SST) response is linked to LIS-induced wind changes and feedbacks in ocean–atmosphere coupling and clouds. ERF and efficacy of LGM GHG are −2.8 W m−2 and 0.9, respectively. The lower efficacy is primarily attributed to a smaller cloud feedback at colder temperatures. Our simulations further demonstrate that the direct ECS calculation using the forcing, efficacy, and temperature response in CESM1.2 overestimates the true value in the model by approximately 25 % due to the neglect of slow ocean dynamical feedback. This is supported by the greater cooling (6.8 ∘C) in a fully coupled LGM simulation than that (5.3 ∘C) in a slab ocean model simulation with ocean dynamics disabled. The majority (67 %) of the ocean dynamical feedback is attributed to dynamical changes in the Southern Ocean, where interactions between upper-ocean stratification, heat transport, and sea-ice cover are found to amplify the LGM cooling. Our study demonstrates the value of climate models in the quantification of climate forcings and the ocean dynamical feedback, which is necessary for an accurate direct ECS estimation.
Highlights
Equilibrium climate sensitivity (ECS) is defined as the global mean surface air temperature (GMST) response to a doubling of atmospheric CO2 and accounts for the Planck response and water vapor, ice albedo, lapse rate, and cloud feedbacks
Our results show that this method is especially well-suited for quantifying the land ice sheets (LISs) forcing and is an advancement over either simplified bulk calculations or the approximate partial radiative perturbation method used in previous studies, which only provide an estimation of the shortwave forcing from albedo effects
For a doubling of CO2, ERFfsst, ERFα, and ERFkernel are 3.7 ± 0.3, 3.9 ± 0.3, and 4.0 W m−2, respectively, well within the multi-model range in recent studies (Smith et al, 2018; Tang et al, 2019). For both Last Glacial Maximum (LGM) greenhouse gases (GHGs) and 2 × CO2, ERFkernel falls in the middle of the uncertainty range of ERFα, suggesting that both the correction methods using radiative kernels and climate sensitivity parameters produce meaningful and accurate results
Summary
Equilibrium climate sensitivity (ECS) is defined as the global mean surface air temperature (GMST) response to a doubling of atmospheric CO2 and accounts for the Planck response and water vapor, ice albedo, lapse rate, and cloud feedbacks (with timescales < 100 years; “Charney Sensitivity”; Charney et al, 1979). We address whether ECS can be accurately estimated using the direct calculation approach and knowledge of the LGM climate forcing and global temperature To answer this question, we adopt the adjusted forcing– feedback framework (Sherwood et al, 2015) to provide a complete quantification of the forcing and efficacy of LGM LIS and GHG using a suite of climate simulations, in comparison to previous studies that only considered surface albedo effects of LIS. We discuss the implications of our results for direct ECS estimation using paleoclimate reconstructions
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